chore: import upstream snapshot with attribution

python/ray/tune/tests/example.py

@@ -0,0 +1,61 @@
+# ruff: noqa
+
+# This is an example quickstart for Tune.
+# To connect to a cluster, uncomment below:
+
+# import ray
+# import argparse
+# parser = argparse.ArgumentParser()
+# parser.add_argument("--address")
+# args = parser.parse_args()
+# ray.init(address=args.address)
+
+# __quick_start_begin__
+from ray import tune
+
+
+def objective(config):  # <1>
+    score = config["a"] ** 2 + config["b"]
+    return {"score": score}
+
+
+search_space = {  # <2>
+    "a": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
+    "b": tune.choice([1, 2, 3]),
+}
+
+tuner = tune.Tuner(objective, param_space=search_space)  # <3>
+
+results = tuner.fit()
+print(results.get_best_result(metric="score", mode="min").config)
+# __quick_start_end__
+
+# __ml_quick_start_begin__
+def objective(step, alpha, beta):
+    return (0.1 + alpha * step / 100) ** (-1) + beta * 0.1
+
+
+def training_function(config):
+    # Hyperparameters
+    alpha, beta = config["alpha"], config["beta"]
+    for step in range(10):
+        # Iterative training function - can be any arbitrary training procedure.
+        intermediate_score = objective(step, alpha, beta)
+        # Feed the score back back to Tune.
+        tune.report({"mean_loss": intermediate_score})
+
+
+tuner = tune.Tuner(
+    training_function,
+    param_space={
+        "alpha": tune.grid_search([0.001, 0.01, 0.1]),
+        "beta": tune.choice([1, 2, 3]),
+    },
+)
+results = tuner.fit()
+
+print("Best config: ", results.get_best_result(metric="mean_loss", mode="min").config)
+
+# Get a dataframe for analyzing trial results.
+df = results.get_dataframe()
+# __ml_quick_start_end__